MATLAB Code Implementation for Radar Signal Sorting

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Radar signal sorting using clustering algorithms for unknown signal classification, merging multiple hypothesis classes via similarity coefficients, and performing pattern fusion for multi-mode radars through temporal correlation analysis with MATLAB implementation approaches

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In the process of radar signal sorting, we can utilize clustering algorithms such as K-means or DBSCAN to classify unknown radar signals based on their parameter characteristics. This implementation typically involves feature extraction from pulse descriptor words (PDWs) including frequency, pulse width, and time of arrival. Additionally, we can employ similarity coefficients like Jaccard index or cosine similarity to merge multiple hypothesis classes, which helps in reducing classification ambiguity. The algorithm can further incorporate temporal correlation analysis to perform pattern fusion for multi-mode radars, where we track signal patterns over time using sliding window techniques or hidden Markov models. This comprehensive approach, implementable through MATLAB's Statistics and Machine Learning Toolbox, ultimately yields more accurate sorting results by combining spatial clustering with temporal pattern recognition.

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